#Get the data Here we look at 538 Halloween candy. We take the CSV

url <-"https://raw.githubusercontent.com/fivethirtyeight/data/master/candy-power-ranking/candy-data.csv"

candy <- read.csv(url,row.names=1)
head(candy, n=5)
##              chocolate fruity caramel peanutyalmondy nougat crispedricewafer
## 100 Grand            1      0       1              0      0                1
## 3 Musketeers         1      0       0              0      1                0
## One dime             0      0       0              0      0                0
## One quarter          0      0       0              0      0                0
## Air Heads            0      1       0              0      0                0
##              hard bar pluribus sugarpercent pricepercent winpercent
## 100 Grand       0   1        0        0.732        0.860   66.97173
## 3 Musketeers    0   1        0        0.604        0.511   67.60294
## One dime        0   0        0        0.011        0.116   32.26109
## One quarter     0   0        0        0.011        0.511   46.11650
## Air Heads       0   0        0        0.906        0.511   52.34146

Q1.

nrow(candy)
## [1] 85

Q2.

sum(candy$fruity)
## [1] 38

#2. What is your favorite candy?

candy["Snickers",]$winpercent
## [1] 76.67378

Q4.

candy["Kit Kat",]$winpercent
## [1] 76.7686

Q5.

candy["Tootsie Roll Snack Bars",]$winpercent
## [1] 49.6535
library("skimr")
skim(candy)
Data summary
Name candy
Number of rows 85
Number of columns 12
_______________________
Column type frequency:
numeric 12
________________________
Group variables None

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
chocolate 0 1 0.44 0.50 0.00 0.00 0.00 1.00 1.00 ▇▁▁▁▆
fruity 0 1 0.45 0.50 0.00 0.00 0.00 1.00 1.00 ▇▁▁▁▆
caramel 0 1 0.16 0.37 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▂
peanutyalmondy 0 1 0.16 0.37 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▂
nougat 0 1 0.08 0.28 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▁
crispedricewafer 0 1 0.08 0.28 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▁
hard 0 1 0.18 0.38 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▂
bar 0 1 0.25 0.43 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▂
pluribus 0 1 0.52 0.50 0.00 0.00 1.00 1.00 1.00 ▇▁▁▁▇
sugarpercent 0 1 0.48 0.28 0.01 0.22 0.47 0.73 0.99 ▇▇▇▇▆
pricepercent 0 1 0.47 0.29 0.01 0.26 0.47 0.65 0.98 ▇▇▇▇▆
winpercent 0 1 50.32 14.71 22.45 39.14 47.83 59.86 84.18 ▃▇▆▅▂

Q6. Is there any variable/column that looks to be on a different scale to the majority of the other columns in the dataset? Win percent

Q7. What do you think a zero and one represent for the candy$chocolate column?

Q8. Plot a histogram of winpercent values

hist(candy$winpercent)

Q9. Is the distribution of winpercent values symmetrical? No

Q10. Is the center of the distribution above or below 50%? Below

Q11. On average is chocolate candy higher or lower ranked than fruit candy? Chocolate candy is higher ranked than fruity candy

First we need to final all the chocolate candy rows in my ‘candy’ data

inds<- as.logical(candy$chocolate)
candy[inds,]$winpercent 
##  [1] 66.97173 67.60294 50.34755 56.91455 38.97504 55.37545 62.28448 56.49050
##  [9] 59.23612 57.21925 76.76860 71.46505 66.57458 55.06407 73.09956 60.80070
## [17] 64.35334 47.82975 54.52645 70.73564 66.47068 69.48379 81.86626 84.18029
## [25] 73.43499 72.88790 65.71629 34.72200 37.88719 76.67378 59.52925 48.98265
## [33] 43.06890 45.73675 49.65350 81.64291 49.52411
inds<- as.logical(candy$chocolate)
chocolate<- candy[inds,]$winpercent 

inds.fruit<- as.logical(candy$fruity)
fruity<- candy[inds.fruit,]$winpercent 

mean(chocolate)
## [1] 60.92153
mean(fruity)
## [1] 44.11974

Q12. Is this difference statistically significant? Yes

t.test(chocolate, fruity)
## 
##  Welch Two Sample t-test
## 
## data:  chocolate and fruity
## t = 6.2582, df = 68.882, p-value = 2.871e-08
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  11.44563 22.15795
## sample estimates:
## mean of x mean of y 
##  60.92153  44.11974

#3. Overall Candy Rankings

Q13. What are the five least liked candy types in this set? Nik L Nip, Boston Baked Beans, Chiclets, Super Bubble, Jawbusters

head(candy[order(candy$winpercent),], n=5)
##                    chocolate fruity caramel peanutyalmondy nougat
## Nik L Nip                  0      1       0              0      0
## Boston Baked Beans         0      0       0              1      0
## Chiclets                   0      1       0              0      0
## Super Bubble               0      1       0              0      0
## Jawbusters                 0      1       0              0      0
##                    crispedricewafer hard bar pluribus sugarpercent pricepercent
## Nik L Nip                         0    0   0        1        0.197        0.976
## Boston Baked Beans                0    0   0        1        0.313        0.511
## Chiclets                          0    0   0        1        0.046        0.325
## Super Bubble                      0    0   0        0        0.162        0.116
## Jawbusters                        0    1   0        1        0.093        0.511
##                    winpercent
## Nik L Nip            22.44534
## Boston Baked Beans   23.41782
## Chiclets             24.52499
## Super Bubble         27.30386
## Jawbusters           28.12744

Q14. What are the top 5 all time favorite candy types out of this set?

head(candy[order(candy$winpercent),], n=)
##                    chocolate fruity caramel peanutyalmondy nougat
## Nik L Nip                  0      1       0              0      0
## Boston Baked Beans         0      0       0              1      0
## Chiclets                   0      1       0              0      0
## Super Bubble               0      1       0              0      0
## Jawbusters                 0      1       0              0      0
## Root Beer Barrels          0      0       0              0      0
##                    crispedricewafer hard bar pluribus sugarpercent pricepercent
## Nik L Nip                         0    0   0        1        0.197        0.976
## Boston Baked Beans                0    0   0        1        0.313        0.511
## Chiclets                          0    0   0        1        0.046        0.325
## Super Bubble                      0    0   0        0        0.162        0.116
## Jawbusters                        0    1   0        1        0.093        0.511
## Root Beer Barrels                 0    1   0        1        0.732        0.069
##                    winpercent
## Nik L Nip            22.44534
## Boston Baked Beans   23.41782
## Chiclets             24.52499
## Super Bubble         27.30386
## Jawbusters           28.12744
## Root Beer Barrels    29.70369

Q15. Make a first barplot of candy ranking based on winpercent values.

library(ggplot2)

ggplot(candy)+
  aes(winpercent, rownames(candy))+
  geom_col()

Make it better >Q16. This is quite ugly, use the reorder() function to get the bars sorted by winpercent?

ggplot(candy)+
  aes(winpercent, reorder(rownames(candy), winpercent))+
  geom_col()

my_cols=rep("black", nrow(candy))
my_cols[as.logical(candy$chocolate)] = "red"
my_cols[as.logical(candy$bar)] = "orange"
my_cols[as.logical(candy$fruity)] = "brown"

ggplot(candy) + 
  aes(winpercent, reorder(rownames(candy),winpercent)) +
  geom_col(fill=my_cols)

>Q17. What is the worst ranked chocolate candy? Sixlets

Q18. What is the best ranked fruity candy? Starburst

#4. Taking a look at pricecenter

library(ggrepel)

# How about a plot of price vs win
ggplot(candy) +
  aes(winpercent, pricepercent, label=rownames(candy)) +
  geom_point(col=my_cols) + 
  geom_text_repel(col=my_cols, size=3.3, max.overlaps = 5)
## Warning: ggrepel: 53 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

ord <- order(candy$pricepercent, decreasing = TRUE)
head( candy[ord,c(11,12)], n=5 )
##                           pricepercent winpercent
## Nik L Nip                        0.976   22.44534
## Nestle Smarties                  0.976   37.88719
## Ring pop                         0.965   35.29076
## HersheyÕs Krackel               0.918   62.28448
## HersheyÕs Milk Chocolate        0.918   56.49050

Q19. Which candy type is the highest ranked in terms of winpercent for the least money - i.e. offers the most bang for your buck? Reese’s Minatures

Q20. What are the top 5 most expensive candy types in the dataset and of these which is the least popular? a. Nik L Nip, Nestle Smarties, Ring pop, Hershey’s Krackel. Hershey’s Milk Chocolate b. Nik L Nip

#5. Exploring the correlation structure

library(corrplot)
## corrplot 0.90 loaded
cij <- cor(candy)
corrplot(cij)

>Q22. What two variables are anti-correlated? chocolate and fruity

Q23. What two varibles are most positvely correlated? chocolate and winpercent

#6. Principal Component Analysis

pca<- prcomp(candy, scale=TRUE)
summary(pca)
## Importance of components:
##                           PC1    PC2    PC3     PC4    PC5     PC6     PC7
## Standard deviation     2.0788 1.1378 1.1092 1.07533 0.9518 0.81923 0.81530
## Proportion of Variance 0.3601 0.1079 0.1025 0.09636 0.0755 0.05593 0.05539
## Cumulative Proportion  0.3601 0.4680 0.5705 0.66688 0.7424 0.79830 0.85369
##                            PC8     PC9    PC10    PC11    PC12
## Standard deviation     0.74530 0.67824 0.62349 0.43974 0.39760
## Proportion of Variance 0.04629 0.03833 0.03239 0.01611 0.01317
## Cumulative Proportion  0.89998 0.93832 0.97071 0.98683 1.00000
plot(pca$x[,1:2], col=my_cols)

plot(pca$x[,1:2], col=my_cols, pch=16)

# Make a new data-frame with our PCA results and candy data

my_data <- cbind(candy, pca$x[,1:3])
p <- ggplot(my_data) + 
        aes(x=PC1, y=PC2, 
            size=winpercent/100,  
            text=rownames(my_data),
            label=rownames(my_data)) +
        geom_point(col=my_cols)

p

library(ggrepel)

p + geom_text_repel(size=3.3, col=my_cols, max.overlaps = 7)  + 
  theme(legend.position = "none") +
  labs(title="Halloween Candy PCA Space",
       subtitle="Colored by type: chocolate bar (dark brown), chocolate other (light brown), fruity (red), other (black)",
       caption="Data from 538")
## Warning: ggrepel: 41 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

library(plotly)
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout
ggplotly(p)
par(mar=c(8,4,2,2))
barplot(pca$rotation[,1], las=2, ylab="PC1 Contribution")

Q24. What original variables are picked up strongly by PC1 in the positive direction? Do these make sense to you? Fruity, hard, pluribus